PostgreSQL Optimization for Django Platform
Budget / Salary₹12,500–37,500
TypeFreelance project
LocationRemote
Posted1 hour ago
Our online education portal, crescentacademyedu.com, is powered by Python—primarily Django with a couple of small Flask services—and everything funnels through a single PostgreSQL instance. Traffic and course data have grown quickly, and the database layer is now the clear bottleneck.
What I need is a specialist who can dive into our existing REST-driven codebase, profile the slow spots, and leave us with a noticeably faster, more stable platform. You’ll be looking at query plans, indexes, connection pooling, and any ORM patterns that might be hurting us. If you prefer working directly in SQL rather than through Django’s ORM when performance calls for it, that’s fine; the end goal is speed and reliability. While the stack is firmly Python/PostgreSQL, I’m open to discussing fresh ideas—whether that means refining our current setup or introducing lightweight services if a Node.js or Rails-style approach would add value without bloating the architecture.
Deliverables
• A concise audit report outlining pain points in our current PostgreSQL schema, queries, and Django ORM usage.
• Implemented optimizations: new or revised indexes, refactored queries, and any necessary migrations or config tweaks (connection pooling, caching layers, etc.).
• Benchmarked before-and-after metrics showing tangible improvements in response times and throughput under simulated production load.
• Clear, step-by-step deployment notes so my team can reproduce the changes in staging and roll them out to production confidently.
Acceptance criteria: key API endpoints must demonstrate at least a 40 % reduction in average response time under typical load tests, with memory and CPU usage remaining within our current server limits.
If this sounds like a challenge you’re ready to tackle, let’s talk details and timelines.
What I need is a specialist who can dive into our existing REST-driven codebase, profile the slow spots, and leave us with a noticeably faster, more stable platform. You’ll be looking at query plans, indexes, connection pooling, and any ORM patterns that might be hurting us. If you prefer working directly in SQL rather than through Django’s ORM when performance calls for it, that’s fine; the end goal is speed and reliability. While the stack is firmly Python/PostgreSQL, I’m open to discussing fresh ideas—whether that means refining our current setup or introducing lightweight services if a Node.js or Rails-style approach would add value without bloating the architecture.
Deliverables
• A concise audit report outlining pain points in our current PostgreSQL schema, queries, and Django ORM usage.
• Implemented optimizations: new or revised indexes, refactored queries, and any necessary migrations or config tweaks (connection pooling, caching layers, etc.).
• Benchmarked before-and-after metrics showing tangible improvements in response times and throughput under simulated production load.
• Clear, step-by-step deployment notes so my team can reproduce the changes in staging and roll them out to production confidently.
Acceptance criteria: key API endpoints must demonstrate at least a 40 % reduction in average response time under typical load tests, with memory and CPU usage remaining within our current server limits.
If this sounds like a challenge you’re ready to tackle, let’s talk details and timelines.
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